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View all- Peng SYamamoto A(2023)Z-TCA: Fast Algorithm for Triadic Concept Analysis Using Zero-suppressed Decision DiagramsJournal of Information Processing10.2197/ipsjjip.31.72231(722-733)Online publication date: 2023
Applying binary decision diagram to extract concepts from triadic formal context
Pages 466 - 469
Abstract
Triadic Concept Analysis (TCA) is an applied mathematical technique for data analysis which the relations between objects, attributes and conditions are identified. However, the volume of information to be processed could make TCA impracticable. For example, with the increasing of social network for personal (Facebook) and professional (LinkedIn) usage, more and more applications of data analysis on environments with high dimensionality (Big Data) have been discussed in the literature. This paper has as an objective to evaluate the behavior of the TRIAS algorithm in order to extract triadic concepts in high dimensional contexts. The experiments shows that our approach has a better performance - up to 33% faster - than its original algorithm.
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Index Terms
- Applying binary decision diagram to extract concepts from triadic formal context
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Published In
March 2020
2348 pages
ISBN:9781450368667
DOI:10.1145/3341105
- Conference Chairs:
- Chih-Cheng Hung,
- Tomas Cerny,
- Program Chairs:
- Dongwan Shin,
- Alessio Bechini
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Published: 30 March 2020
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Cited By
View all- Peng SYamamoto A(2023)Z-TCA: Fast Algorithm for Triadic Concept Analysis Using Zero-suppressed Decision DiagramsJournal of Information Processing10.2197/ipsjjip.31.72231(722-733)Online publication date: 2023
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